CN101918862B

Movatterモバイル変換

Info

Publication number: CN101918862B
Application number: CN2008801247451A
Authority: CN
Inventors: 威廉·S·哈蒙
Original assignee: Terraspark Geosciences LP
Current assignee: Cgg Jason (holland) Co
Priority date: 2007-11-14
Filing date: 2008-11-14
Publication date: 2013-05-08
Anticipated expiration: 2028-11-14
Also published as: AU2008322505A1; US20090122061A1; CA2822231A1; CN103278847A; RU2010123794A; AU2008322505B9; EP2624014A2; WO2009065036A1; CN101918862A; EP2624014A3; AU2008322505B2; CA2705197C; RU2549213C2; EP2220517A1; EP2220517A4; CA2705197A1

Abstract

A set of processes and tools for preprocessing data prior to seismic interpretation, comprising: voxel connectivity delineation, seismic response reduction, voxel suppression, and voxel density. Voxel connectivity is used to help remove unimportant data. Seismic response reduction is used to reduce the seismic response of a given reflector to a lobe (e.g., the main lobe). Voxel suppression helps highlight and enhance rock boundaries to aid human and computer-aided interpretation. Voxel density scores the local importance of data trends within a volume (e.g., a 3D seismic volume) to help enhance these trends.

Description

Translated fromChinese

地震数据处理Seismic Data Processing

相关申请信息Related application information

根据美国法典第35篇第119条(e)，本申请主张于2007年11月14日提交的、题为“Seismic Data Processing”的美国专利申请No.60/987,906的权益和优先权，并且涉及PCT申请PCT/US2007/071733(公开为WO2008/005690)，在此通过整体引用并入两者的全部。Pursuant to Title 35, United States Code, Section 119(e), this application claims the benefit and priority of U.S. Patent Application No. 60/987,906, filed November 14, 2007, entitled "Seismic Data Processing," and relates to PCT application PCT/US2007/071733 (published as WO2008/005690), both of which are hereby incorporated by reference in their entirety.

背景技术Background technique

在上述相关应用中，描述帮助潜在的碳氢化合物矿层(hydrocarbondeposit)的识别的处理(process)，包括执行三维地震体(volume)的结构解释，将三维地震体转换为地层切片体，执行包括边界面和断层(fault)的提取和将地层切片体转换为空间域的地层切片体的地层学解释。如所图示的，在相关应用的图24a中呈现域转换之前的示例性地震体，在相关应用的图24b中呈现在转换中使用的解释的层位(horizon)和断层，并且在相关应用的图24c中呈现域转换的地层切片体。在相关应用的图24a中的输入地震体具有与同时(syn-)和之后(post-)沉积断层作用(faulting)相关联的变形。输出域转换体(相关应用的图24c)基本没有变形。In the related application above, a process to aid in the identification of potential hydrocarbon deposits is described, including performing structural interpretation of 3D seismic volumes, converting 3D seismic volumes into stratigraphic slice volumes, performing Extraction of interfaces and faults and stratigraphic interpretation of stratigraphic slices converted to spatial domain stratigraphic slices. As illustrated, an exemplary seismic volume prior to domain transformation is presented in Figure 24a of the relevant application, the interpreted horizons and faults used in the transformation are presented in Figure 24b of the relevant application, and in Figure 24b of the relevant application Domain-transformed stratigraphic slice volumes are presented in Figure 24c of . The input seismic volume in Figure 24a of a related application has deformations associated with simultaneous (syn-) and post-) sedimentary faulting. The output domain transition body (Fig. 24c of related application) is substantially undistorted.

用于从3D地震体识别和解释沉积环境、沉积系统和沉积系统的元素的该工作流程和自动或半自动方法和系统受益于数据预处理。The workflow and automatic or semi-automatic methods and systems for identifying and interpreting depositional environments, depositional systems and elements of depositional systems from 3D seismic volumes benefit from data pre-processing.

发明内容Contents of the invention

本发明的一个方面是提供用于数据处理的系统、方法和技术。One aspect of the present invention is to provide systems, methods and techniques for data processing.

本发明的另一方面是提供用于地震数据预处理的系统、方法和技术。Another aspect of the present invention is to provide systems, methods and techniques for seismic data preprocessing.

本发明的另一方面是提供用于3D地震数据预处理的系统、方法和技术。Another aspect of the present invention is to provide systems, methods and techniques for 3D seismic data preprocessing.

本发明的另一方面指向确定体素连通性(voxel connectivity)分数。Another aspect of the invention is directed to determining voxel connectivity scores.

本发明的另一方面涉及基于体素连通性分数减少数据“混乱(clutter)”。Another aspect of the invention relates to reducing data "clutter" based on voxel connectivity scores.

本发明另外的示例性方面涉及减少反射体到瓣(lobe)的地震响应。Additional exemplary aspects of the invention relate to reducing reflector-to-lobe seismic response.

本发明另外的示例性方面涉及减少反射体到主瓣的地震响应。Additional exemplary aspects of the invention relate to reducing the seismic response of reflectors to the main lobe.

本发明的另一示例性方面指向移除地震数据中的无关反射。Another exemplary aspect of the invention is directed to removing extraneous reflections in seismic data.

本发明另外的示例性方面涉及突出和增强岩石边界以帮助地震数据的解释。Additional exemplary aspects of the invention relate to highlighting and enhancing rock boundaries to aid interpretation of seismic data.

本发明另外的方面涉及评分和利用3D数据体中的置信度(confidence)。Additional aspects of the invention relate to scoring and utilizing confidence in 3D data volumes.

本发明另外的方面涉及使用本地数据冗余来生成和输出数据集中置信度的稳定评估。Additional aspects of the invention relate to the use of local data redundancy to generate and output robust estimates of confidence in a data set.

本发明的这些和其他特征和优点在示例性实施例的以下详细描述中描述，或从示例性实施例的以下详细描述显而易见。These and other features and advantages of the invention are described in, or are apparent from, the following detailed description of the exemplary embodiments.

附图说明Description of drawings

将参照附图详细描述本发明的示例性实施例。应该理解，附图不必按比例示出。在某些实例中，对于本发明的理解不必需或使得其他细节难以察觉的细节可能已经被省略。当然，应该理解，本发明不必限于在此图示的特定实施例。Exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the drawings are not necessarily drawn to scale. In certain instances, details which are not necessary to an understanding of the invention or which obscure other details may have been omitted. It should be understood, of course, that the invention is not necessarily limited to the particular embodiments illustrated herein.

图1图示根据本发明的示例性数据处理系统和地震解释系统。Figure 1 illustrates an exemplary data processing system and seismic interpretation system according to the present invention.

图2图示根据本发明的用于确定体素连通性的示例性方法。Figure 2 illustrates an exemplary method for determining voxel connectivity according to the present invention.

图3图示根据本发明的用于减少反射的示例性方法。FIG. 3 illustrates an exemplary method for reducing reflections according to the present invention.

图4图示在如地震体的数据体中突出高幅度事件的示例性方法。4 illustrates an exemplary method of highlighting high magnitude events in a data volume, such as a seismic volume.

图5图示示例性体素密度估计处理。Figure 5 illustrates an exemplary voxel density estimation process.

图6(a-f)图示具有逐渐更高的连通性分数阈值的体素连通性到地震数据的示例性应用：a-输入“稀疏”地震部分；b-f-输入通过具有从b中的100到f中的20000逐渐增加的连通性分数的体素连通性滤波的地震数据；Figure 6(a-f) illustrates an exemplary application of voxel connectivity to seismic data with progressively higher connectivity score thresholds: a - input "sparse" seismic section; 20,000 voxel-wise connectivity-filtered seismic data with increasing connectivity scores in

图7(a-c)图示具有逐渐更高的连通性分数阈值的体素连通性到地震数据的示例性应用：a-输入“稀疏”地震部分；b-c-输入通过具有逐渐增加的连通性分数的体素连通性滤波的地震数据；Figure 7(a-c) illustrates an exemplary application of voxel connectivity to seismic data with progressively higher connectivity score thresholds: a - input "sparse" seismic section; b-c - input via Voxel connectivity filtered seismic data;

图8(a-c)图示具有逐渐更高的连通性分数阈值的体素连通性到不同于图2中使用的相同地震数据体的垂直地震部分的示例性应用：a-输入“稀疏”地震部分；b-c-输入通过具有逐渐增加的连通性分数的体素连通性滤波的地震数据；Figure 8(a-c) illustrates an exemplary application of voxel connectivity with progressively higher connectivity score thresholds to vertical seismic portions of the same seismic data volume different from those used in Figure 2: a - Input "sparse" seismic portion ; b-c - input seismic data filtered by voxel connectivity with progressively increasing connectivity scores;

图9图示以反射体为中心的示例性“零相位”小波-注意主瓣上下的低振幅侧瓣的列；Figure 9 illustrates an exemplary "zero phase" wavelet centered on a reflector - note the columns of low amplitude side lobes above and below the main lobe;

图10(a-d)图示使用反射衰减处理的示例性地震部分：a-输入数据；b-反射衰减处理的部分；c-仅用于峰值的反射衰减处理的部分；d-仅用于波谷的反射衰减处理的部分；Figure 10(a-d) illustrates an exemplary seismic section using reflection attenuation processing: a - input data; b - section of reflection attenuation processing; c - section of reflection attenuation processing for peaks only; d - only for troughs The part of reflection attenuation processing;

图11(a-d)图示应用到稀疏输入数据集的示例性反射衰减：a-输入数据；b-反射衰减处理的部分；c-仅用于峰值的反射衰减处理的部分；d-仅用于波谷的反射衰减处理的部分；Figure 11(a-d) illustrates exemplary reflection decay applied to a sparse input dataset: a - input data; b - part of reflection decay processing; c - part of reflection decay processing for peaks only; d - only for The part of the reflection attenuation processing of the trough;

图12图示用于维度5×9的二维矩形算子的余弦锥重定比例因子的示例；FIG. 12 illustrates an example of a cosine cone rescaling factor for a two-dimensional rectangular operator ofdimension 5×9;

图13(a-b)图示应用到示例地震体的体素抑制的示例：a-来自输入地震体的部分；b-来自用体素抑制滤波的体的相同部分；Figure 13(a-b) illustrates an example of voxel suppression applied to an example seismic volume: a - part from input seismic volume; b - same part from volume filtered with voxel suppression;

图14(a-b)图示应用到与图13中相同的示例地震体的体素抑制的示例：a-来自输入地震体的部分；b-来自用体素抑制滤波的体的相同部分，该部分与图13中显示的部分正交；Figure 14(a-b) illustrates an example of voxel suppression applied to the same example seismic volume as in Figure 13: a - part from input seismic volume; b - same part from volume filtered with voxel suppression, the part Orthogonal to that shown in Figure 13;

图15(a-b)图示应用到第二示例地震体的体素抑制的示例：a-来自输入地震体的部分；b-来自用体素抑制滤波的体的相同部分；Figure 15(a-b) illustrates an example of voxel suppression applied to a second example seismic volume: a - part from input seismic volume; b - same part from volume filtered with voxel suppression;

图16(a-c)图示对于10×10数据阵列的体素密度计算的数值结果的示例：a-输入二维数据阵列；b-使用接受大于或等于6的所有输入值的3×3体素密度算子处理的输出二维数据阵列；c-进一步将输出密度分数约束为大于或等于4的结果；Figure 16(a-c) illustrate examples of numerical results of voxel density calculations for 10x10 data arrays: a - input 2D data array; b - use 3x3 voxels accepting all input values greater than or equal to 6 The output two-dimensional data array processed by the density operator; c-further constrain the output density score to be greater than or equal to the result of 4;

图17(a-b)图示对来自图16a的10×10数据阵列的体素密度计算的数值结果的示例：a-在中心体素落入指定阈值范围的额外约束的情况下，如图16b中处理的输出二维数据阵列；b-应用4的最小阈值到图17a中的密度分数的结果；Figure 17(a-b) illustrate examples of numerical results of voxel density calculations for the 10×10 data array from Figure 16a: a - with the additional constraint that the central voxel falls within a specified threshold range, as in Figure 16b Processed output 2D data array; b - result of applying a minimum threshold of 4 to the density score in Figure 17a;

图18(a-d)图示在图16和17中描述的结果的图形表示的示例：a-与图16a相同的原始数据；b-图16c所示的结果；c-图17b所示的结果；d-应用到输入数据的简单的阈值操作的结果，其中没有执行密度计算；Figure 18(a-d) illustrates an example of a graphical representation of the results described in Figures 16 and 17: a - the same raw data as in Figure 16a; b - the results shown in Figure 16c; c - the results shown in Figure 17b; d - the result of a simple thresholding operation applied to the input data, where no density calculation is performed;

图19(a-f)图示一些标准数据平滑算子与密度引导的平滑的示例性比较：a、b-分别应用3×3平均值和中值滤波器到图18a中的原始数据的结果；c-仅应用平均值平滑到最小密度测试失败的体素的结果；d-仅应用中值算子到最小密度测试失败的体素的结果；e、f-修改选择性平滑以仅包括落在初始指定的阈值范围外部的体素的结果；Figures 19(a-f) illustrate exemplary comparisons of some standard data smoothing operators with density-guided smoothing: a, b - results of applying 3x3 mean and median filters, respectively, to the raw data in Figure 18a; c - the result of applying mean smoothing only to voxels that failed the minimum density test; d - the result of applying only the median operator to voxels that failed the minimum density test; e, f - modified selective smoothing to include only those that fall within the initial Results for voxels outside the specified threshold range;

图20(a-d)图示应用到通过示出峡谷的连续或连贯(coherence)体的层片(horizontal slice)的各种示例性描述的滤波器：a-输入数据；b-移除具有低于指定截止值的密度分数的体素；c-应用置信度自适应平滑的结果，其中最小密度测试失败并且在有效阈值范围外部的体素包括在平滑中；d-应用对比增强到a中数据的结果；20(a-d) illustrate filters applied to various exemplary descriptions of horizontal slices by showing a continuous or coherence volume of the canyon: a - input data; b - remove voxels with density fractions for the specified cutoff; c - the result of applying confidence adaptive smoothing, where voxels that fail the minimum density test and are outside the effective threshold range are included in the smoothing; d - apply contrast enhancement to the data in a result;

图21图示应用到通过示出河道的地层体的平片的各种示例性描述的滤波器：a-输入数据；b-a中数据的曲率响应；c-应用密度阈值滤波到b中的曲率数据的结果；d-应用对比增强到b中的曲率数据的结果；Figure 21 illustrates filters applied to various exemplary descriptions of a flat slice through a formation volume showing a channel: a - input data; b - curvature response of data in a; c - application of density threshold filtering to curvature data in b d - the result of applying contrast enhancement to the curvature data in b;

图22(a-b)图示应用对比增强到来自图16a的采样数据阵列的示例性数值和图形结果：a-在来自图16a的输入原始数据的直方图的情况下，来自对比增强的数值输出阵列；b-在输出的对比增强数据的直方图的情况下，来自对比增强的图形输出阵列；Figure 22(a-b) illustrates exemplary numerical and graphical results of applying contrast enhancement to the sampled data array from Figure 16a: a - Numerical output array from contrast enhancement in the case of a histogram of input raw data from Figure 16a ; b - in the case of a histogram of output contrast-enhanced data, from the contrast-enhanced graphics output array;

图23(a-e)图示在来自墨西哥湾数据集的时间片上局部自适应体素密度受控平滑和对比增强的示例性效果：a-应用3×3中值滤波器以降低随机噪声情况下的原始地震数据；b-对a中数据计算连贯性的结果；c-计算b中数据的变化的结果；d、e-应用由c中的变化分布控制的局部自适应对比增强(d)和平滑(e)到b中数据的结果；Figure 23(a-e) illustrate exemplary effects of locally adaptive voxel density controlled smoothing and contrast enhancement on time slices from the Gulf of Mexico dataset: a - where a 3×3 median filter is applied to reduce random noise Raw seismic data; b - result of computing coherence on data in a; c - result of computing changes in data in b; d, e - application of local adaptive contrast enhancement (d) and smoothing controlled by the distribution of changes in c (e) to the result of the data in b;

图24(a-d)在来自图23中使用的墨西哥湾数据集的更深时间片上局部自适应体素密度受控平滑和对比增强的示例性效果：a-应用3×3中值滤波器以降低随机噪声情况下的原始地震数据；b-对a中数据计算连贯性的结果；c、d-应用局部自适应对比增强(c)和平滑(d)到b中数据的结果；Figure 24(a-d) Exemplary effects of locally adaptive voxel density-controlled smoothing and contrast enhancement on deeper time slices from the Gulf of Mexico dataset used in Figure 23: a - 3 × 3 median filter applied to reduce random Raw seismic data in the noisy case; b - the result of computing coherence on the data in a; c, d - the result of applying local adaptive contrast enhancement (c) and smoothing (d) to the data in b;

图25(a-f)图示应用对比增强到断层增强计算的输出上的连贯性数据的示例性效果：a-在周围断层的情况下，示出盐体部分的连贯性时间片；b、c-应用两级对比增强到a中数据的结果；d-使用原始连贯性数据(a)作为输入的断层增强输出；e-使用来自(b)的对比增强数据作为输入的断层增强输出；f-使用对比增强数据(c)作为输入的断层增强输出；Figure 25(a-f) illustrate exemplary effects of applying contrast enhancement to coherence data on the output of fault enhancement calculations: a - showing coherence time slices of salt body sections in case of surrounding faults; b, c - Result of applying two levels of contrast enhancement to the data in a; d - output of tomographic enhancement using original coherence data (a) as input; e - output of tomographic enhancement using contrast-enhanced data from (b) as input; f - output of tomographic enhancement using Contrast-enhanced data (c) as input tomography-enhanced output;

图26(a-d)图示应用到连贯性的体素密度的示例。面板(a)包含海底峡谷的连贯性图像。面板(b)示出应用二进制体素密度滤波到面板(a)中数据的结果。最小密度阈值测试失败的体素被分配空值。面板(c)示出体素密度受控的平滑的结果。体素密度分数用于改变面板(d)中的数据对比。体素密度受控平滑和对比增强保持数据的原始环境，而不是简单移除密度阈值测试失败的体素；26(a-d) illustrate examples of voxel density applied to coherence. Panel (a) contains a coherent image of the submarine canyon. Panel (b) shows the result of applying binary voxel density filtering to the data in panel (a). Voxels that failed the minimum density threshold test were assigned a null value. Panel (c) shows the results of voxel density-controlled smoothing. Voxel density fractions were used to alter the data contrast in panel (d). Voxel density controlled smoothing and contrast enhancement preserves the original context of the data, rather than simply removing voxels that fail the density threshold test;

图27(a-b)图示体素抑制结果的示例。面板(a)包含原始振幅部分。平层扁平盐体的顶部和底部由面板(a)中的箭头指示。面板(b)示出应用体素抑制到面板(a)中数据的结果。Figure 27(a-b) illustrate examples of voxel suppression results. Panel (a) contains raw amplitude components. The top and bottom of the stratified flat salt bodies are indicated by the arrows in panel (a). Panel (b) shows the result of applying voxel suppression to the data in panel (a).

图28(a-d)图示应用到稀疏地震数据的反射衰减的示例；28(a-d) illustrate examples of reflection attenuation applied to sparse seismic data;

图29(a-c)图示应用体素连通性的示例性结果；以及29(a-c) illustrate exemplary results of applying voxel connectivity; and

图30(a-d)图示应用到实际数据的工作流的示例。30(a-d) illustrate an example of a workflow applied to actual data.

具体实施方式Detailed ways

关于数据(特别是地震数据)的处理和解释，将描述本发明的示例性实施例。然而，应该认识到，通常本发明的系统和方法对于代表任何环境、对象或物品的任何类型的数据将同样有效。Exemplary embodiments of the present invention will be described with respect to the processing and interpretation of data, particularly seismic data. However, it should be appreciated that in general the systems and methods of the present invention will work equally well with any type of data representing any environment, object or item.

还将关于地震数据解释和操纵描述本发明的示例性系统和方法。然而，为了避免不必要地模糊本发明，以下描述省略了可能以框图形式示出或另外概述的公知结构和设备。Exemplary systems and methods of the present invention will also be described with respect to seismic data interpretation and manipulation. However, to avoid unnecessarily obscuring the present invention, the following description omits well-known structures and devices that may be shown in block diagram form or otherwise outlined.

为了说明的目的，阐述大量细节以便提供本发明的彻底理解。然而，应该认识到，本发明可以以超出在此阐述的具体细节的多种方式实践。For purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the invention. It should be appreciated, however, that the invention may be practiced in numerous ways beyond the specific details set forth herein.

此外，尽管在此图示的示例性实施例示出了配置系统的各种组件，但是要认识到，系统的各种组件可以位于分布式网络(如通信网络和/或因特网)的遥远部分，或在专用安全、不安全和/或加密系统内。因此，应该认识到，系统的组件可以组合为一个或多个设备或配置在分布式网络(如通信网络)的特定节点上。如将从以下描述认识到的，并且为了计算效率的原因，系统的组件可以安排在分布式网络内的任何位置而不影响系统的操作。Furthermore, while the exemplary embodiments illustrated herein show various components of the configuration system, it is to be appreciated that the various components of the system may be located in remote parts of a distributed network, such as a communication network and/or the Internet, or Within dedicated secure, unsecured and/or encrypted systems. Accordingly, it should be appreciated that components of the system may be combined into one or more devices or deployed at specific nodes in a distributed network, such as a communication network. As will be appreciated from the description below, and for reasons of computational efficiency, the components of the system may be arranged anywhere within the distributed network without affecting the operation of the system.

此外，应该认识到，各种链路可以用于连接各元件，并且可以是有线或无线链路，或者其任何组合，或者任何其它已知或之后开发的(各)元件，其能够提供数据到连接的元件和/或从连接的元件通信数据。如在此使用的术语模块可以指任何已知或之后开发的硬件、软件、固件或其组合，其能够执行与该元件相关联的功能。如在此使用的术语确定(determine)、计算(calculate)和计算(compute)和其变体可互换地使用，并且包括任何类型的方法、处理(process)、数学操作或技术，包括由系统(如处理器)、专家系统或神经网络执行的那些。Furthermore, it should be appreciated that various links may be used to connect the elements, and may be wired or wireless links, or any combination thereof, or any other known or later developed element(s) capable of providing data to Connected elements and/or communicate data from connected elements. The term module as used herein may refer to any known or later developed hardware, software, firmware or combination thereof that is capable of performing the functionality associated with that element. As used herein the terms determine, calculate and compute and variations thereof are used interchangeably and include any type of method, process, mathematical operation or technique, including (such as processors), expert systems, or those implemented by neural networks.

此外，在此识别的所有参考文献在此通过整体引用并入。Additionally, all references identified herein are hereby incorporated by reference in their entirety.

图1图示经由链路连接到地震解释系统200的示例性数据处理系统100。地震解释系统200可以帮助盐体、峡谷、河道(channel)、层位或表面覆盖等的一个或多个的解释，如在上述相关应用中描述的。数据处理系统100包括体素连通性模块(voxel connectivity module)110、反射衰减(reflection collapse)模块120、控制器130、存储140、一个或多个计算机可读存储介质150(其上可以存储体现在此公开的技术的软件，并且与控制器、存储器、I/O接口和存储协作执行)、体素抑制模块160、体素密度模块170、存储器180和I/O接口190，所有通过(各)链路(未示出)连接。系统还可以与输出设备(如计算机显示器300)相关联，在输出设备上可以将各种技术的输出显示给用户。FIG. 1 illustrates an exemplary data processing system 100 connected via a link to aseismic interpretation system 200 .Seismic interpretation system 200 may facilitate interpretation of one or more of salt bodies, canyons, channels, horizons, or surface coverage, etc., as described in the related applications above. Data processing system 100 includes voxel connectivity module (voxel connectivity module) 110, reflection decay (reflection collapse) module 120, controller 130, storage 140, one or more computer-readable storage media 150 (on which can be stored software of this disclosed technology, and executed in cooperation with the controller, memory, I/O interface, and storage), voxel suppression module 160, voxel density module 170, memory 180, and I/O interface 190, all through (each) Links (not shown) connect. The system can also be associated with an output device, such as computer display 300, on which the output of various technologies can be displayed to the user.

体素连通性模块110帮助连通体素的绘制(mapping)。可以通过设计为移除不重要数据点的数据处理步骤致使地震数据体稀疏(sparse)。类似地，一些地震属性导致大的空或未定义数据区域。在这两种情况下，空(null)或未定义区域一般用通过用于创建体的处理步骤“漏(leak)”的不重要数据加斑点(speckle)。该视觉混乱可能使用于在体中存在的重要特征的分割或用户或计算机解释的这种体的使用复杂。该视觉混乱的一些或全部的移除是用于提高这些数据体的效用(utility)的一个示例性目的。The voxel connectivity module 110 facilitates the mapping of connected voxels. Seismic data volumes can be rendered sparse through data processing steps designed to remove unimportant data points. Similarly, some seismic attributes result in large empty or undefined data regions. In both cases, null or undefined regions are generally speckled with unimportant data that "leaks" through the processing steps used to create the volume. This visual clutter can complicate the segmentation of important features present in the volume or the use of such volumes for user or computer interpretation. The removal of some or all of this visual clutter is an exemplary purpose for increasing the utility of these data volumes.

体素连通性绘制模块110的操作的示例性实施例确定哪些体素是数据体中连通特征的构成成分(constituent member)。“连通性分数”(多少体素构成该特征)然后可以通过为输出体制定最小特征大小阈值而用于移除识别为小的、并且因此不重要的特征。An exemplary embodiment of the operation of the voxel connectivity mapping module 110 determines which voxels are constituent members of connected features in the data volume. The "connectivity score" (how many voxels make up the feature) can then be used to remove features identified as small, and therefore unimportant, by enforcing a minimum feature size threshold for the output volume.

体素连通性的示例性实施例绘制出体中所有连通的非空体素。在绘制连通体素之后，定义体中的每个连通特征的连通性分数作为其构成体素的数目。然后通过从具有低于某最小阈值的连通性分数的输出数据体移除特征，可以滤波视觉混乱。以此方式，从数据体移除小特征，然后可以输出和保存该数据体。An exemplary embodiment of voxel connectivity maps out all connected non-empty voxels in a volume. After drawing connected voxels, define the connectivity score of each connected feature in the volume as the number of its constituent voxels. Visual clutter can then be filtered by removing features from the output data volume with connectivity scores below some minimum threshold. In this way, small features are removed from the data volume, which can then be exported and saved.

如果体素连通性应用到稀疏振幅(amplitude)体，则振幅极性可以用作对于连通性绘制的额外可选约束。例如，如果绘制出正振幅反射，则仅正振幅被认为是非空的。If voxel connectivity is applied to a sparse amplitude volume, then amplitude polarity can be used as an additional optional constraint on connectivity mapping. For example, if positive amplitude reflections are plotted, only positive amplitudes are considered non-null.

图6演示逐渐更高的连通性分数阈值到相同数据集的应用。图6a包含由分离处理(例如，体素抑制)致使稀疏的原始地震数据。盐底辟(salt diapir)存在于数据集的中心。已经维持重要振幅事件，同时移除低振幅反射。然而，相当大量的分散的、非连通的数据点保留在数据集中。图6b示出移除由少于100连通体素组成的特征的结果。该结果表现在减少视觉混乱量中的显著改进。图6c、6d、6e和6f使用逐渐更高的连通性分数阈值滤出特征。图6f过度积极(aggressive)(使用20000的连通性分数阈值)，并且已经移除盐(salt)边界反射的部分。Figure 6 demonstrates the application of progressively higher connectivity score thresholds to the same dataset. Figure 6a contains raw seismic data rendered sparse by separation processing (eg, voxel suppression). A salt diapir exists in the center of the dataset. Significant amplitude events have been maintained while low amplitude reflections are removed. However, a considerable number of scattered, disconnected data points remain in the dataset. Figure 6b shows the result of removing features consisting of less than 100 connected voxels. The results represent a significant improvement in reducing the amount of visual clutter. Figures 6c, 6d, 6e and 6f filter out features using progressively higher connectivity score thresholds. Figure 6f is overly aggressive (using a connectivity score threshold of 20,000) and has removed salt boundary reflections.

图7和8包含来自相同数据集的不同截面视图。在每个图中，面板(a)示出通过上述相同预处理使得稀疏的原始数据。面板(b)和(c)分别示出最小滤波的数据版本和更积极滤波的结果。在图7b和7c中，重要的反射已经保持，同时已经减少视觉混乱。使用200的连通性分数阈值生成示例性结果。类似地，图7c保持重要反射，同时用800的连通性分数阈值更积极地滤波。然而，在图8c中相同的阈值已经过分滤波。在数据部分的基础的更不连通但是仍重要的反射已经移除，因此，应该小心选择用于滤波的连通性分数阈值。Figures 7 and 8 contain different cross-sectional views from the same dataset. In each figure, panel (a) shows the raw data made sparse by the same preprocessing described above. Panels (b) and (c) show minimally filtered versions of the data and results of more aggressive filtering, respectively. In Figures 7b and 7c, important reflections have been maintained while visual clutter has been reduced. Exemplary results were generated using a connectivity score threshold of 200. Similarly, Figure 7c maintains important reflections while filtering more aggressively with a connectivity score threshold of 800. However, in Fig. 8c the same threshold has been overfiltered. The more disconnected but still important reflections at the base of the data part have been removed, therefore, the connectivity score threshold used for filtering should be chosen carefully.

来自体素连通性模块110的输出体允许用户从稀疏数据集移除不需要的视觉混乱。连通体基于构成体素的数目记分。然后从输出数据体移除具有低于用户指定的阈值的连通性分数的特征。该技术可以是用于准备用于地震解释(如分割)的数据集的强大工具。The output volumes from voxel connectivity module 110 allow users to remove unwanted visual clutter from sparse datasets. Connectivity is scored based on the number of constituent voxels. Features with connectivity scores below a user-specified threshold are then removed from the output data volume. This technique can be a powerful tool for preparing datasets for seismic interpretation such as segmentation.

地震反射由主振幅响应和若干更小侧边振幅响应组成。不同于主响应的这些额外响应使用于重要反射体的计算机分割的振幅体的使用复杂。在多数处理的地震数据体中，使用如图9所示的零相位小波。这意味着实际反射体位置由主反射响应(或瓣(lobe))的最大值的位置指示。零相位小波还关于最大反射瓣对称。在感兴趣的主瓣上下都存在其他无关瓣。移除这些额外的反射瓣可导致用于高振幅事件的人工和计算机解释的更纯净体。Seismic reflections consist of a main amplitude response and several smaller side amplitude responses. These additional responses to the main responses complicate the use of computer-segmented amplitude volumes for important reflectors. In most processed seismic data volumes, a zero-phase wavelet as shown in Figure 9 is used. This means that the actual reflector position is indicated by the position of the maximum of the main reflective response (or lobe). The zero-phase wavelet is also symmetric about the largest reflection lobe. There are other unrelated lobes above and below the main lobe of interest. Removing these extra reflection lobes can lead to a purer body for human and computer interpretation of high-amplitude events.

反射衰减模块120的操作的示例性实施例减小给定反射体对主瓣的地震响应。这移除可能对于地震体中高振幅反射的解释不必要的“混乱”。计算机解释处理和算法也通过从地震数据体移除无关反射受辅助。An exemplary embodiment of the operation of the reflection attenuation module 120 reduces the seismic response of a given reflector to the main lobe. This removes "clutter" that may be unnecessary for the interpretation of high amplitude reflections in the seismic volume. Computer interpretation processing and algorithms are also aided by removing extraneous reflections from the seismic data volume.

在其最基本的示例性形式中，反射衰减模块120例如与控制器130协作卷积1D算子与输入体。对于每个算子位置，运行测试以查看算子的中心体素是否具有算子内包含的所有体素的最高绝对振幅。如果最高绝对振幅体素不是算子的中心，则不做什么并且算子移动到下一体素。然而，如果最高绝对振幅在算子的中心，则模块将该体素值写到其原始位置中的输出体。然后执行两个搜索以确定该反射瓣的范围。In its most basic exemplary form, reflection attenuation module 120 convolutes a 1D operator with an input volume, for example in cooperation with controller 130 . For each operator location, a test is run to see if the operator's center voxel has the highest absolute amplitude of all voxels contained within the operator. If the highest absolute amplitude voxel is not the center of the operator, then do nothing and the operator moves to the next voxel. However, if the highest absolute amplitude is at the center of the operator, the module writes that voxel value to the output volume in its original location. Two searches are then performed to determine the extent of this reflection lobe.

从中心体素向上执行第一搜索。该搜索向上延伸直到遇到零交叉。然后该搜索停止。在向下方向以类似方式执行第二搜索。以此方式，主反射瓣的全部范围由该模块写到输出体。A first search is performed upwards from the center voxel. The search extends upwards until a zero crossing is encountered. Then the search stops. A second search is performed in a similar manner in the downward direction. In this way, the full extent of the main reflection lobe is written by the module to the output volume.

还执行其他步骤，以便确保模块性能的稳定性。振幅中的局部变化以及随机噪声可能导致反射侧瓣局部具有比主反射瓣更大的绝对振幅。为了避免这将不连续引入主反射瓣，可以执行预处理步骤以规则化存在的所有反射瓣的振幅。Additional steps are performed to ensure stability of module performance. Local variations in amplitude as well as random noise may cause reflection side lobes to locally have larger absolute amplitudes than the main reflection lobe. To avoid this introducing discontinuities into the main reflection lobe, a preprocessing step can be performed to normalize the amplitudes of all reflection lobes present.

在该示例性预处理步骤中，使用连通极性分析绘制每个反射瓣的所有成分体素。连通极性分析类似于连通阈值分析，在于其确定3D体中哪些体素连通。差别在于不同于阈值范围的使用，体素的极性是用于确定连通性的唯一参数。一旦绘制反射瓣的所有成分体素，就计算该瓣的平均振幅。该平均值是用于确定反射中的哪个瓣是主反射瓣的振幅值。然后上述主要处理用于移除反射的侧瓣。In this exemplary preprocessing step, all constituent voxels of each reflection lobe are mapped using connected polarity analysis. Connectivity polarity analysis is similar to connectivity threshold analysis in that it determines which voxels in a 3D volume are connected. The difference is that, unlike the use of a threshold range, the polarity of a voxel is the only parameter used to determine connectivity. Once all constituent voxels of a reflection lobe are mapped, the mean amplitude of that lobe is calculated. This average is the amplitude value used to determine which lobe in the reflection is the main reflection lobe. The main processing described above is then used to remove the reflected side lobes.

图10和11图示反射衰减模块对输入地震数据体的应用。这些视图是通过数据集的垂直截面。在数据部分的中间存在盐的水平(horizontal)带。10 and 11 illustrate the application of the reflection attenuation module to an input seismic data volume. These views are vertical sections through the dataset. There is a horizontal band of salt in the middle of the data section.

图10a包含用于该部分的原始数据。存在高振幅和低振幅反射。图10b示出应用反射衰减模块处理之后的结果。已经从数据移除反射的侧瓣。然而，反射振幅的一些局部变化已经导致对其保持反射瓣作为主瓣的不一致行为。图10c和10d分别图示在反射衰减处理中仅考虑正或负振幅的结果。例如，当试图增强具有已知反射系数极性的界面(如沉积物/盐界面)时，这可能是希望的。Figure 10a contains the raw data for this section. There are high and low amplitude reflections. Figure 10b shows the result after applying the reflection attenuation module processing. Reflected side lobes have been removed from the data. However, some local variations in reflection amplitudes have resulted in inconsistent behavior for which the reflection lobe is maintained as the main lobe. Figures 10c and 10d illustrate the results of considering only positive or negative amplitudes, respectively, in the reflection attenuation process. This may be desirable, for example, when trying to enhance an interface with known reflectance polarity, such as a sediment/salt interface.

图11a示出图10a中数据的稀疏版本。通过设计为从数据集移除低振幅反射的分离处理使得数据稀疏。图11b示出当考虑该稀疏数据集中的所有反射时的反射衰减技术的性能。图11c和11d分别图示仅选择性地考虑正或负振幅的结果。在每个数据示例中成功移除反射侧瓣，但是当仅考虑一个极性时结果可能更稳定。Figure 11a shows a sparse version of the data in Figure 10a. The data were made sparse by a separation process designed to remove low-amplitude reflections from the dataset. Figure lib shows the performance of the reflection decay technique when all reflections in this sparse dataset are considered. Figures 11c and 11d illustrate the results of selectively considering only positive or negative amplitudes, respectively. Reflection side lobes were successfully removed in every data example, but results may be more stable when only one polarity is considered.

示例性反射衰减模块120从地震数据体中的各反射体移除一个或多个侧瓣(主瓣上和/或下)。这从用于解释高振幅反射的体移除不必要的混乱。这些高振幅反射的人工解释和计算机分割可以受益于这些数据处理技术。The example reflection attenuation module 120 removes one or more side lobes (above and/or below the main lobe) from each reflector in the seismic data volume. This removes unnecessary confusion from the volume used to account for high amplitude reflections. Human interpretation and computer segmentation of these high-amplitude reflections can benefit from these data processing techniques.

地震数据体中岩石边界的表示可能非常复杂。在盐或成岩边界的情况下，他们可以以任何可想象的方位和配置刺穿(cut through)数据集。当用手执行时，这种界面的手动解释可能是极端耗时的。这种类型的解释的自动化是地震数据解释中非常重要的研究目标。体素抑制是朝向突出和增强岩石边界以辅助它们的人工和计算机自动化解释的第一步。The representation of rock boundaries in a seismic data volume can be very complex. In the case of salt or diagenetic boundaries, they can cut through the dataset in any imaginable orientation and configuration. Manual interpretation of such interfaces can be extremely time consuming when performed by hand. The automation of this type of interpretation is a very important research goal in seismic data interpretation. Voxel suppression is the first step towards highlighting and enhancing rock boundaries to aid their manual and computer automated interpretation.

体素抑制的示例性实施例是用于强调地震体中高振幅事件的方法。这通过致使体稀疏的体素抑制模块160完成，同时局部维持它们的原始位置中的高振幅事件。保持的体素值可以可选地重定比例(rescale)，以便增高弱事件的强度。该重定比例规则化遍及体的重要反射的表示。An exemplary embodiment of voxel suppression is a method for emphasizing high amplitude events in a seismic volume. This is done by the voxel suppression module 160 rendering the volume sparse, while locally maintaining high amplitude events in their original location. The preserved voxel values can optionally be rescaled in order to increase the intensity of weak events. This rescaling regularizes the representation of important reflections throughout the volume.

示例性体素抑制模块160卷积3维算子与输入地震数据体。对于每个算子位置，算子内的所有体素通过绝对值分类。用户指定的(经由用户输入设备(未示出)输入)百分比的最高值保持在它们的原始位置。该保持的百分比一般小；对于所有应用小于15％。The exemplary voxel suppression module 160 convolves the 3-dimensional operator with the input seismic data volume. For each operator position, all voxels within the operator are classified by absolute value. The highest values of the percentages specified by the user (entered via a user input device (not shown)) remain at their original positions. This retained percentage is generally small; less than 15% for all applications.

如果用户偏好，则这些保持的值可以通过体素抑制模块160重定比例，以便规则化遍及体的局部重要反射的表示。这通过计算算子内包含的所有体素的标准偏差，并且将这些值重定比例以使得局部标准偏差匹配整个体的标准偏差来完成。为了完成这，所有保持的体素乘以重定比例因子(RF)。RF计算为：If user prefers, these retained values can be rescaled by the voxel suppression module 160 in order to regularize the representation of locally significant reflections throughout the volume. This is done by computing the standard deviation of all voxels contained within the operator, and rescaling these values so that the local standard deviation matches the standard deviation of the whole volume. To do this, all remaining voxels are multiplied by a rescaling factor (RF). RF is calculated as:

RF＝整个体标准偏差/算子标准偏差RF = overall population standard deviation / operator standard deviation

在一些情况下，增高体素值，而在其它情况下，它们可能降低。最终结果是体内保持的所有特征具有类似的外观。In some cases, the voxel values are increased, while in other cases they may be decreased. The end result is a similar appearance of all features maintained in vivo.

数据还可以由体素抑制模块160重定比例，以便对算子的中心的体素给出强调。径向余弦锥可以用于对算子的中心而不是在其边缘的保留体素给出更多强调。该余弦锥基于它们距算子中心的距离对体素重定比例。通过1的因子将中心体素重定比例(没有改变)。最末梢的体素通过0的因子重定比例(归零，zeroed out)。在之间，正弦锥可以为算子内包含的每个个别体素定义重定比例因子。图12是用于维度5×9的矩形算子的余弦锥重定比例因子的2维示例。The data may also be rescaled by voxel suppression module 160 to give emphasis to the voxel in the center of the operator. The radial cosine cone can be used to give more emphasis to the center of the operator rather than the preserved voxels at its edges. The cosine cone rescales voxels based on their distance from the operator center. Rescales the central voxel by a factor of 1 (no change). The last voxel is rescaled by a factor of 0 (zeroed out). In between, the sinusoidal cone can define a rescaling factor for each individual voxel contained within the operator. Figure 12 is a 2-dimensional example of a cosine cone rescaling factor for a rectangular operator of dimension 5x9.

该示例性的步骤组合可以局部增强高振幅反射，同时移除无关的周围体素。结果是视觉上更清楚的体，其更容易为了自动计算的目的通过其它属性增强。This exemplary combination of steps can locally enhance high-amplitude reflections while removing extraneous surrounding voxels. The result is a visually clearer volume that is easier to enhance with other properties for automatic calculation purposes.

图13、14和15示出对真实数据体应用体素抑制的结果。这些视图是穿过它们各自数据集的垂直截面。图13和14是来自相同数据体的不同截面。它们每个具有在数据部分中间的水平盐带。图15是来自不同测量，并且具有在视图中心的盐底辟。Figures 13, 14 and 15 show the results of applying voxel suppression to real data volumes. These views are vertical sections through their respective datasets. Figures 13 and 14 are different sections from the same data volume. They each have a horizontal salt band in the middle of the data section. Figure 15 is from a different survey and has the salt diapir in the center of the view.

图13a示出在运行体素抑制模块160应用体素抑制之前的原始数据体。通过体素抑制技术保持局部高振幅反射(图13b)。类似地，图14b保持原始数据部分(图14a)中存在的局部高振幅反射。Figure 13a shows the raw data volume before running the voxel suppression module 160 to apply voxel suppression. Local high-amplitude reflections were preserved by voxel suppression techniques (Fig. 13b). Similarly, Figure 14b preserves the localized high-amplitude reflections present in the original data portion (Figure 14a).

图15a包含来自另一测量的原始地震数据。图15b示出对于该第二数据体的体素抑制技术的结果。在每个实际数据示例中保持重要反射，特别那些与数据体内包含的主要岩石对比(例如，盐体)相关联的。Figure 15a contains raw seismic data from another survey. Figure 15b shows the results of the voxel suppression technique for this second data volume. Important reflections are maintained in each actual data example, especially those associated with the main rock contrasts (eg, salt bodies) contained within the data volume.

因此，体素抑制模块160的操作的一个示例性操作实施例是运行与整个体卷积的窗口算子。对于每个算子位置，执行一系列示例性处理步骤。它们是：Thus, one exemplary operational embodiment of the operation of the voxel suppression module 160 is to run a window operator convolved with the entire volume. For each operator location, a series of exemplary processing steps are performed. They are:

基于绝对值分类体素，Classify voxels based on absolute value,

重定比例所有体素值，以使得局部算子的标准偏差匹配全局标准偏差，rescales all voxel values so that the standard deviation of the local operator matches the global standard deviation,

保持用户指定百分比上的重定比例值(归零所有其它值)，maintain the rescaling value at a user-specified percentage (zeroing all other values),

使用余弦锥基于算子内的位置为保持的值定比例，以及use the cosine cone to scale the held value based on the position within the operator, and

在它们的原始位置输出变为锥形的值。Outputs the values that become tapered in their original positions.

以此方式，保持局部重要振幅事件，并且给出规则的表示，同时移除不重要反射。得到的保存体是稀疏的，仅包括重定比例的最高振幅反射体。In this way, locally important amplitude events are kept and given a regular representation, while unimportant reflections are removed. The resulting preserved volumes are sparse, including only the rescaled highest-amplitude reflectors.

体素抑制模块160通过遍及体除了最重要的反射外移除全部，致使体稀疏。得到的体强调主要声阻抗边界。这些高阻抗对比将存在于主要岩石改变。如此，体素抑制的应用可以是用于突出如盐边界的复杂界面的有用的第一步。The voxel suppression module 160 renders the volume sparse by removing all but the most significant reflections throughout the volume. The resulting volume emphasizes the main acoustic impedance boundaries. These high impedance contrasts will exist for major rock changes. As such, the application of voxel suppression can be a useful first step for highlighting complex interfaces like salt boundaries.

从3D地震数据体计算的属性通常在它们的地质学趋势的表现中是嘈杂和混乱的。复杂的形态学和地质特征的表示可导致用于突出感兴趣特征的给定属性的不一致性能。结构和成岩套印(overprinting)也可能使属性结果复杂。Attributes computed from 3D seismic data volumes are often noisy and chaotic in their representation of geological trends. Representation of complex morphological and geological features can lead to inconsistent performance of a given attribute for highlighting features of interest. Structural and diagenetic overprinting can also complicate property results.

噪声的处理和不均匀属性性能的规则化是潜在的非常重要的研究目标。体素密度技术的示例性实施例是用于评分3D地震体内数据趋势的局部重要性的方式。然后可以增强或规格化重要区域，同时抑制或滤出不重要区域。The handling of noise and the regularization of inhomogeneous property properties are potentially very important research goals. An exemplary embodiment of a voxel density technique is a means for scoring the local importance of trends in 3D seismic in vivo data. Important regions can then be enhanced or normalized, while unimportant regions are suppressed or filtered out.

体素密度模块170的示例性操作实施例包括通过运行窗口模块执行的运行窗口算法。对于每个算子位置，计数落入给定阈值范围内的窗口内的数据点的数目；产生密度分数。高密度分数的区域视为具有高置信度。相反，低密度分数的区域假定为噪声并且被滤出或削弱。可以通过从低密度分数的区域移除数据点来过滤噪声。通过较不积极地平滑高置信度区域，可以在平滑期间保持重要边缘。还可以属在性体中增强体对比；增高信噪(S/N)比。An exemplary operational embodiment of the voxel density module 170 includes a running windowing algorithm performed by the running windowing module. For each operator position, count the number of data points falling within a window within a given threshold; yielding a density score. Regions with high density scores are considered to have high confidence. In contrast, regions of low density fractions are assumed to be noise and are filtered out or attenuated. Noise can be filtered by removing data points from regions of low density scores. By smoothing high confidence regions less aggressively, important edges can be preserved during smoothing. It can also be used to enhance the body contrast in the sex body; increase the signal-to-noise (S/N) ratio.

多种技术可以用于在数据体中控制噪声。平均值和中值滤波是对于随机噪声有效的滤波方法。类似地，小波变换是用于随机噪声滤波的另一强大工具。然而，噪声不是困扰属性结果的唯一问题。不均匀性能可能是对于快速利用属性结果的更大阻碍。Various techniques can be used to control noise in the data volume. Mean and median filtering are effective filtering methods for random noise. Similarly, the wavelet transform is another powerful tool for random noise filtering. However, noise is not the only problem plaguing attribute results. Uneven performance can be an even bigger impediment to quickly exploiting attribute results.

地质学的事实很少反映概念模型的简单性。没有由概念模型说明的因素通常干扰(confuse)设计来反映(image)给定地质特征的属性。进一步使属性性能复杂的是由地震测量反映的比例(scale)的多样性。子地震分辨率特征可以将调谐效果引入不可通过许多属性从噪声区分的数据。图21b示出从变平的数据体计算的河道的曲率响应。尽管视觉上有用，但是缺少河道的统一表示使手动解释复杂，并且阻止河道解释的任何自动处理。Facts of geology rarely reflect the simplicity of conceptual models. Factors not accounted for by the conceptual model often confuse the design to image the properties of a given geological feature. Further complicating attribute performance is the diversity of scales reflected by seismic measurements. Subseismic resolution features can introduce tuning effects into data that is not distinguishable from noise by many attributes. Figure 21b shows the curvature response of the channel calculated from the flattened data volume. Although visually useful, the lack of a unified representation of channels complicates manual interpretation and prevents any automatic processing of channel interpretation.

示例性体素密度技术使用局部数据冗余来创建数据集中置信度的稳定估计。数据体中感兴趣的特征通常在每个方向持续一些距离。这些特征的持续可以用于克服它们在给定数据体中的不均匀表示。这通过3D算子与数据集的卷积完成。通过体素密度模块170为所有体素计算置信度的度量。该置信度分数然后可以用于引导滤波和增强操作。Exemplary voxel density techniques use local data redundancy to create stable estimates of confidence in a dataset. Features of interest in a data volume typically persist for some distance in each direction. The persistence of these features can be used to overcome their uneven representation in a given data volume. This is done by convolution of the 3D operator with the dataset. A measure of confidence is computed for all voxels by the voxel density module 170 . This confidence score can then be used to guide filtering and enhancement operations.

示例性体素密度模块170将3D算子与输入数据体卷积。对于运行窗口算子的每个位置，计数落入给定阈值范围的体素的数目。该计数操作的结果是窗口的中心体素的密度分数。高密度分数指示高置信度的体素。低密度分数突出低置信度的体素。以此方式，可以实现体(或属性)质量的稳定的、无混乱的估计。用户可以选择重要的密度值的具体范围，并且突出值以高浓度存在的区域。The exemplary voxel density module 170 volumetrically convolutes the 3D operator with the input data. For each location where the window operator is run, count the number of voxels that fall within a given threshold range. The result of this count operation is the density score for the center voxel of the window. A high density score indicates a voxel with high confidence. Low density scores highlight voxels with low confidence. In this way, a stable, confusion-free estimation of volume (or property) quality can be achieved. The user can select specific ranges of important density values and highlight regions where values exist in high concentrations.

图16演示对于10×10数据阵列的体素密度计算的数值结果。图16a示出包含0和9之间值的原始数据阵列。存在由阵列中的灰带指示的高值的斜的(diagonal)趋势。图16b包含从使用接受大于6的所有值对3×3算子的计算得到的密度分数。通过由可能采样的最大数目除以算子中采样的局部数目的比率缩放密度分数来处理边缘效应。因此，在角上，原始密度通过将其乘以9(总的算子大小)除以4(在该位置的算子中的采样的数目)的比率来校正。图16c然后包含将输出密度分数限制为4或更大值的结果。该阈值引入滤出低置信度区域的能力。Figure 16 demonstrates the numerical results of voxel density calculations for a 10x10 data array. Figure 16a shows a raw data array containing values between 0 and 9. There is a diagonal trend of high values indicated by the gray band in the array. Figure 16b contains the density scores from calculations using the 3x3 operator accepting all values greater than 6. Edge effects are handled by scaling the density fraction by the ratio of the maximum number of possible samples divided by the local number of samples in the operator. So, in the corner, the raw density is corrected by multiplying it by the ratio of 9 (total operator size) divided by 4 (number of samples in the operator at that location). Figure 16c then contains the results of limiting the output density fraction to values of 4 or greater. This threshold introduces the ability to filter out low confidence regions.

密度估计可以以两种示例性方式确定。在第一，体素密度模块170为每个算子位置确定密度分数。这是在图16中使用的计算方式。在第二，中心体素必须落入用于要计算的密度分数的指定阈值范围。该额外限制导致较不平滑的密度分数体，但是具有不模糊数据中存在的趋势的优点。该密度评分的中心通过方法的使用使得体素密度处理保持边缘。为不满足中心通过预定条件的体素输出空值。图17a包含在继续密度计算之前要求中心体素落入合适的阈值范围的10×10采样阵列的密度分数结果。图17b包含应用4的最小阈值到图17a中的密度分数的结果。在图16c中存在的“混乱”已经被移除，并且更聚焦于高值趋势(由灰带突出)的表示。Density estimates can be determined in two exemplary ways. In the first, the voxel density module 170 determines a density score for each operator location. This is the calculation used in Figure 16. In the second, the central voxel must fall within a specified threshold range for the density score to be calculated. This additional constraint results in a less smooth density fraction volume, but has the advantage of not obscuring trends present in the data. The use of the density-scored center-pass method keeps voxel density processing marginal. Outputs a null value for voxels that do not satisfy the predetermined criteria for center passage. Figure 17a contains the density score results for a 10x10 sampled array requiring the center voxel to fall within the appropriate threshold range before proceeding with the density calculation. Figure 17b contains the result of applying a minimum threshold of 4 to the density scores in Figure 17a. The "clutter" present in Figure 16c has been removed and more focused on the representation of high value trends (highlighted by gray bands).

图18包含上述结果的图形表示。图18a包含如图16a的相同原始数据。图18b是为最小密度阈值阵列(图16c)中的每个非空位置输出原始数据阵列值的结果。图18c添加要求中心体素落入有效阈值范围(类似于图17b)的元素。图18d示出其中不执行密度计算的简单阈值操作的结果。这可以认为是来自接受所有6或更大值的1×1算子的体素密度结果。明显地，这些结果次于图18c中存在的那些结果。该差别演示了将阈值与置信度的3D估计组合的协同作用。在这个意义上，体素密度可以认为是空间加权阈值操作。Figure 18 contains a graphical representation of the above results. Figure 18a contains the same raw data as Figure 16a. Figure 18b is the result of outputting raw data array values for each non-empty position in the minimum density threshold array (Figure 16c). Figure 18c adds an element requiring the central voxel to fall within the effective threshold range (similar to Figure 17b). Figure 18d shows the result of a simple thresholding operation where no density calculation is performed. This can be thought of as the voxel density result from the 1×1 operator that accepts all values of 6 or greater. Clearly, these results are inferior to those present in Figure 18c. This difference demonstrates the synergy of combining thresholding with 3D estimates of confidence. In this sense, voxel density can be thought of as a spatially weighted thresholding operation.

当应用到数据体时，体素密度产生密度分数体。该体类似于在图16和17中存在的结果。密度分数体是可视化数据中存在的不同趋势的重要性的方式。增强具有连贯的、持续的数据趋势的区域，同时削弱不连贯区域。When applied to a data volume, voxel density produces a density fractional volume. This volume is similar to the results present in FIGS. 16 and 17 . Density fraction volumes are a way of visualizing the significance of different trends present in your data. Enhances areas with coherent, consistent data trends while weakening incoherent areas.

密度分数体还可以认为是体积置信度估计。在该数据置信度估计的情况下，可以执行各种操作。这些操作包括噪声滤波、边缘保持平滑和体对比增强的一个或多个。Density fraction volumes can also be thought of as volume confidence estimates. In the case of this data confidence estimate, various operations can be performed. These operations include one or more of noise filtering, edge preserving smoothing, and volume contrast enhancement.

输入数据体可以使用体素密度模块170以各种方式修改和增强。密度分数体可以认为是对于体中趋势的置信度估计。使用该置信度估计，可能通过体素值的密度分数引导重定比例增强体。阈值滤波可以移除不感兴趣的数据。还可能控制平滑度，其中平滑低置信度区域多于高置信度区域。The input data volume can be modified and enhanced in various ways using voxel density module 170 . Density score volumes can be thought of as confidence estimates for trends in the volume. Using this confidence estimate, it is possible to guide the rescaling augmentation by the density fraction of voxel values. Threshold filtering removes uninteresting data. It is also possible to control smoothness, where low confidence regions are smoothed more than high confidence regions.

通过密度阈值滤波Filtering by Density Thresholding

可以通过移除具有低于指定截止值的密度分数的体素完成二进制滤波。以此方式，可以移除不重要数据区域。由空值取代具有低于指定最小值的密度分数的体素。这在图18b和18c中的数值阵列上演示。图20b示出该处理应用到海底峡谷的连贯性图像的结果。类似地，图21c包含将相同过程应用到变平的河道的曲率图像(图21b)的结果。Binary filtering can be done by removing voxels with a density score below a specified cutoff. In this way, unimportant data areas can be removed. Replaces voxels with a density score below the specified minimum by a null value. This is demonstrated on the numerical arrays in Figures 18b and 18c. Figure 20b shows the result of this process applied to a coherent image of a submarine canyon. Similarly, Figure 21c contains the results of applying the same process to the curvature image of a flattened channel (Figure 21b).

密度引导的平滑Density-guided smoothing

体素是否通过最小密度阈值的相同标准可以用于控制数据体内的平滑操作。通过比通过最小密度测试的体素更多地平滑最小密度测试失败的体素，可以削弱不重要的数据区域。还可能控制哪些体素包括在平滑操作中。The same criterion of whether a voxel passes a minimum density threshold can be used to control the smoothing operation within the data volume. Unimportant regions of data can be attenuated by smoothing voxels that fail the minimum density test more than voxels that pass the minimum density test. It is also possible to control which voxels are included in the smoothing operation.

图19a和19b包含分别应用3×3平均值和中值滤波器到图18a中的原始数据的结果。图19c示出仅应用平均值平滑到最小密度测试失败的体素的结果。通过测试的体素被认为是高置信度的，并且不平滑。类似地，图19d包含仅应用中值算子到最小密度测试失败的体素的结果。图19e和19f示出修改选择性平滑以仅包括落在初始阈值范围外部的体素的结果。在两种情况下，这具有抑制平滑区域中的值(使它们变暗)的结果。这导致更大的视觉对比，以及改进的用于采样阵列的S/N比。明显地，图19e和19f包含优于图18a中原始数据阵列的平滑的结果。Figures 19a and 19b contain the results of applying 3x3 mean and median filters, respectively, to the raw data in Figure 18a. Figure 19c shows the results of applying mean smoothing only to voxels that failed the minimum density test. Voxels that pass the test are considered high confidence and not smooth. Similarly, Figure 19d contains the results of applying the median operator only to voxels that failed the minimum density test. Figures 19e and 19f show the results of modifying the selective smoothing to only include voxels that fall outside the initial threshold range. In both cases, this has the effect of suppressing values in smooth regions (darkening them). This results in greater visual contrast, and an improved S/N ratio for the sampling array. Clearly, Figures 19e and 19f contain smoother results than the original data array in Figure 18a.

图20c包含应用该置信度自适应平滑到海底峡谷的连贯性图像的结果。仅平滑最小密度测试失败的体素。仅仅落在有效阈值范围外部的体素包括在平滑中。应该注意，该自适应平滑的方法已经保持在峡谷中的边缘存在的精细细节。Figure 20c contains the results of applying this confidence adaptive smoothing to a coherence image of a submarine canyon. Only voxels that fail the minimum density test are smoothed. Only voxels that fall outside the effective threshold range are included in the smoothing. It should be noted that the adaptive smoothing method already preserves the fine details present at the edges of the canyon.

数据体的对比增强Contrast Enhancement of Data Volumes

体素通过最小密度测试还是最小密度测试失败可以用于控制数据体的重定比例。通过的体素通过大于1的因子重定比例。测试失败的那些通过小于1的因子重定比例。精确的重定比例因子依赖于原始数据值以及该体素的密度分数。在概念上，每个体素通过其自身值和其重定比例朝向的极值之间的差的百分比来重定比例。通过两个等式控制百分比重定比例。对于通过最小密度测试的体素：Whether a voxel passes or fails the minimum density test can be used to control the rescaling of the data volume. Passed voxels are rescaled by a factor greater than 1. Those that fail the test are rescaled by a factor less than 1. The exact rescaling factor depends on the original data value as well as the density fraction of that voxel. Conceptually, each voxel is rescaled by the percentage of the difference between its own value and the extreme value it rescales towards. Percentage rescaling is controlled by two equations. For voxels that pass the minimum density test:

Ratiopass＝1+(Dscore-Dneutral)/(Nvalues-Dneutral)Ratiopass＝1+(Dscore-Dneutral)/(Nvalues-Dneutral)

对于最小密度测试失败的体素，重定比例比率等式是：For voxels that fail the minimum density test, the rescaling ratio equation is:

Ratiofail＝(Dneutral-Dscore)/DneutralRatiofail=(Dneutral-Dscore)/Dneutral

该比率然后乘以原始体素值以获得重定比例的体素值。“重定比例强度”术语的添加考虑更精细的重定比例操作。图22包含应用这种类型的重定比例到采样数据阵列的数值和图形结果。图16a中高值的趋势(由灰带突出)已经增强，同时抑制围绕其的值的分散。图22b具有比图18a中的原始阵列更改进的S/N比。还示出数据直方图上的效果。This ratio is then multiplied by the original voxel value to obtain the rescaled voxel value. The addition of the term "rescaling strength" allows for finer-grained rescaling operations. Figure 22 contains the numerical and graphical results of applying this type of rescaling to an array of sampled data. The tendency for high values in Figure 16a (highlighted by the gray band) has been enhanced while suppressing the dispersion of values around it. Figure 22b has a more improved S/N ratio than the original array in Figure 18a. The effect on the histogram of the data is also shown.

图20d示出应用该操作到海底峡谷的连贯性图像的结果。用0.5的强度进行该重定比例。对图21d的河道的曲率图像执行相同类型的对比增强。如在自适应平滑的情况下，在数据中已经保持重要边缘，同时改进S/N比。Figure 20d shows the result of applying this operation to a coherence image of a submarine canyon. This rescaling is performed with an intensity of 0.5. The same type of contrast enhancement was performed on the curvature image of the channel of Figure 21d. As in the case of adaptive smoothing, important edges have been preserved in the data while improving the S/N ratio.

局部自适应操作locally adaptive operation

还可能修改重定比例操作以仅局部重定比例高值。通过将阈值范围链接到数据体中的局部变化，仅仅局部高体素将在密度计算中计数。这避免高噪声背景压倒体素密度处理，并且提供更健壮的结果，其中特征的特性体素值显著变化。图23b是示出一系列断层的连贯性图像。可以视觉地确定单个阈值范围不能在不允许许多周围噪声也被增强的情况下表现存在的所有的断层的部分。重要数据更多通过它们的线性趋势被识别，并且是局部高值。通过将阈值范围链接到局部变化，可以增强这些断层，同时削弱周围噪声。It is also possible to modify the rescaling operation to only locally rescale high values. By linking the threshold range to local variations in the data volume, only locally high voxels will be counted in the density calculation. This avoids a noisy background from overwhelming the voxel density processing, and provides more robust results where the characteristic voxel values of features vary significantly. Figure 23b is a coherence image showing a series of slices. It can be determined visually that a single threshold range cannot represent all the faults present without allowing much surrounding noise to be enhanced as well. Important figures are more identified by their linear trends and are local high values. By linking the threshold range to local variations, these faults can be enhanced while attenuating surrounding noise.

图23示出在来自墨西哥湾数据集的时间片上局部自适应体素密度受控平滑和对比增强的效果。图23a包含已经应用轻(3×3)中值滤波器以降低随机噪声的原始数据。图23b是到数据体的连贯性的结果。图23d和23e使用由图23c中可见的变化分布控制的局部可变阈值来产生。具有更高变化的区域(趋向更浅的灰)使得在密度计算中使用更紧的阈值范围。图23d和23e分别包含图23b中的原始连贯性片的局部自适应对比增强和平滑。Figure 23 shows the effect of locally adaptive voxel density controlled smoothing and contrast enhancement on time slices from the Gulf of Mexico dataset. Figure 23a contains raw data to which a light (3x3) median filter has been applied to reduce random noise. Figure 23b is the result of the coherence to the data volume. Figures 23d and 23e were generated using a locally variable threshold controlled by the variation distribution seen in Figure 23c. Regions with higher variation (towards lighter grays) cause tighter threshold ranges to be used in density calculations. Figures 23d and 23e contain local adaptive contrast enhancement and smoothing, respectively, of the original coherent slice in Figure 23b.

图24演示应用到相同体的更深时间片的相同操作。单个阈值范围将不成功捕获图24b中存在的所有重要数据趋势。因此，用于体素密度计算的局部自适应变化受控方法产生好的结果。图24c和24d分别包含对比增强和平滑结果。Figure 24 demonstrates the same operation applied to deeper slices of the same volume. A single threshold range would not successfully capture all of the significant data trends present in Figure 24b. Therefore, locally adaptive variation-controlled methods for voxel density calculations yield good results. Figures 24c and 24d contain contrast enhancement and smoothing results, respectively.

图25a是示出在周围断层的情况下盐体部分的连贯性图像。图25b和25c示出对比增强数据。保持断层的整体趋势，同时周围的不重要数据区域在值上减小。这些数据体然后用断层增强(增强连贯性体中浸渍平面特征的属性)处理。图25d是用于原始连贯性图像的断层增强输出。其受多个假轮廓(lineament)妨碍。当从对比增强的连贯性体处理断层增强体时，这些无关数据大大减少。图25f具有三个断层增强体的最高S/N比。它从最高对比体计算(图25c)。Figure 25a is a coherence image showing a portion of a salt body with surrounding faults. Figures 25b and 25c show contrast enhanced data. The overall trend of the fault is maintained, while the surrounding unimportant data regions are reduced in value. These data volumes are then processed with fault enhancement (enhancing the properties of impregnated planar features in the coherence volume). Figure 25d is the tomographic enhancement output for the original coherence image. It is hampered by multiple false lineaments. These extraneous data are greatly reduced when processing tomographically enhanced volumes from contrast-enhanced coherent volumes. Figure 25f has the highest S/N ratio of the three fault enhancements. It was calculated from the highest contrast volume (Fig. 25c).

体素密度模块提供在数据体中评分置信度的方式。落入给定阈值范围的体素的数目在运动3D算子内计数。该计数结果是算子的中心体素的密度分数。具有高密度分数的体素视为重要的，而那些具有低密度分数的可以视为噪声。可以保持或增强重要数据区域，同时平滑或滤出不重要数据区域。The voxel density module provides a way to score confidence in a data volume. The number of voxels falling within a given threshold range is counted within the motion 3D operator. The result of this count is the density fraction of the central voxel of the operator. Voxels with high density scores are considered important, while those with low density scores can be considered noise. Important data regions can be maintained or enhanced while unimportant data regions are smoothed or filtered out.

体素密度引导的平滑和重定比例操作是保持边缘的。重要趋势可以增强，同时维持它们的整体形状和内部纹理。这通过比有效数据趋势更多地选择性平滑不重要区域来完成。类似地，重要的数据趋势可以选择性地向上增益，同时消减周围噪声。这样的操作保持数据的原始风味，但是具有增加的S/N比。Voxel density-guided smoothing and rescaling operations are edge-preserving. Important trends can be enhanced while maintaining their overall shape and inner texture. This is done by selectively smoothing unimportant regions more than valid data trends. Similarly, important data trends can be selectively boosted upwards while attenuating surrounding noise. Such an operation maintains the original flavor of the data, but has an increased S/N ratio.

密度评分中包括的阈值范围可以链接到数据体中的局部变化。以此方式，保持局部重要的数据趋势。这允许体素密度用于具有在数据区域之间变化的重要数据值范围的数据集。The threshold range included in the density score can be linked to local variations in the data volume. In this way, locally important data trends are maintained. This allows voxel densities to be used for datasets with significant ranges of data values that vary between data regions.

当用于为了视觉和自动解释而预处理数据时，体素密度表示潜在的很有价值的工具。例如，至少可以改进S/N比，并且通过选择性平滑可以给予重要趋势视觉强调。Voxel density represents a potentially valuable tool when used to preprocess data for visual and automated interpretation. For example, at least the S/N ratio can be improved, and important trends can be given visual emphasis by selective smoothing.

图2图示根据本发明确定体素连通性的示例性实施例。具体地，控制在步骤S200开始，并且继续到步骤S210。在步骤S210，输入如地震数据体的数据体。接下来，在步骤S220，绘制连通的非空体素。然后，在步骤S230，根据构成体素的数目确定连通性分数。然后控制继续到步骤S240。Figure 2 illustrates an exemplary embodiment of determining voxel connectivity according to the present invention. Specifically, control begins at step S200 and continues to step S210. In step S210, a data volume such as a seismic data volume is input. Next, in step S220, connected non-empty voxels are drawn. Then, in step S230, a connectivity score is determined according to the number of constituent voxels. Control then continues to step S240.

在可选步骤S240，基于选择的连通性分数，可以过滤特征。类似地，在步骤S250，如果特征在连通性范围内，则可以过滤它们。控制然后继续到步骤S260。At optional step S240, based on the selected connectivity scores, features may be filtered. Similarly, at step S250, if the features are within the connectivity range, they can be filtered. Control then continues to step S260.

在步骤S260，输出和保存视觉混乱减少的地震数据体。控制然后继续到步骤S270，在此控制序列结束。In step S260, the visually clutter-reduced seismic data volume is output and saved. Control then continues to step S270, where the control sequence ends.

图3图示根据本发明的减少反射的示例性方法。具体地，控制在步骤S300开始，并且继续到步骤S310。在步骤S310，输入地震数据体。例如，输入的数据体可以是在图2所示的处理中保存的数据体。接下来，在步骤S320，所有反射瓣的振幅可以可选地规则化，控制继续到步骤S330。Figure 3 illustrates an exemplary method of reducing reflections according to the present invention. Specifically, control begins at step S300 and continues to step S310. In step S310, a seismic data volume is input. For example, the input data volume may be the data volume saved in the process shown in FIG. 2 . Next, at step S320, the amplitudes of all reflection lobes may optionally be regularized and control continues to step S330.

在步骤S330，对于每个算子位置，执行步骤S332-S338。具体地，在步骤S332，进行确定算子的中心体素是否具有算子内的所有体素的最高绝对振幅。接下来，在步骤S334，如果最高振幅体素不在算子的中心，则处理移动到下一体素。In step S330, for each operator position, steps S332-S338 are performed. Specifically, in step S332, a determination is made whether the central voxel of the operator has the highest absolute amplitude of all voxels within the operator. Next, at step S334, if the highest amplitude voxel is not at the center of the operator, the process moves to the next voxel.

在步骤S336，如果最高绝对振幅体素处于算子的中心，则将体素值写到其原始位置中的输出体。接下来，在步骤S337，从中心体素执行向上和向下搜索，以确定主反射瓣的范围。然后，在步骤S338，将主反射瓣的整个范围保存到输出。控制然后继续到步骤S340，在此控制序列结束。In step S336, if the highest absolute amplitude voxel is at the center of the operator, the voxel value is written to the output volume in its original location. Next, in step S337, an upward and downward search is performed from the center voxel to determine the range of the main reflection lobe. Then, at step S338, the entire extent of the main reflection lobe is saved to the output. Control then continues to step S340, where the control sequence ends.

图4图示体素抑制的示例性方法，在输入体(如图3中保存的体)的情况下控制在步骤S400开始，并且继续到步骤S410。在步骤S410，对于每个算子位置，通过绝对值分类所有体素。接下来，在步骤S420，用户指定值之上的体素保持在它们的原始位置。然后，在步骤S430，根据RF＝整个体标准偏差/算子标准偏差可选地执行用于规则化的重定比例。控制然后继续到步骤S440。FIG. 4 illustrates an exemplary method of voxel suppression, control starting at step S400 in the case of an input volume such as the one saved in FIG. 3 , and continuing to step S410 . In step S410, for each operator position, all voxels are sorted by absolute value. Next, at step S420, voxels above the user-specified value are kept at their original positions. Then, at step S430, rescaling for regularization is optionally performed according to RF=overall volume standard deviation/operator standard deviation. Control then continues to step S440.

在步骤S440，算子中心的体素被重定比例，使得它们被强调。接下来，在步骤S450，输出和保存视觉改进的体。In step S440, the voxels at the center of the operator are rescaled such that they are emphasized. Next, in step S450, the visually improved volume is output and saved.

图5图示示例性体素密度确定方法。在输入体(如图4中保存的体)的情况下控制在步骤S500开始，并且继续到步骤S510。在步骤S510，对于运行窗口的每个位置，计数给定阈值范围内的体素数目。接下来，在步骤S520，输出窗口的中心体素的密度分数。这通过子例程S522和S524完成。具体地，在步骤S522，确定对于每个算子位置的密度分数。然后，在步骤S524，如果中心体素落入指定阈值范围，则计算密度分数，更高的密度分数与高置信度的体素相关，并且更低密度分数与更低置信度的体素相关。Figure 5 illustrates an exemplary voxel density determination method. In the case of an input volume (such as the volume saved in FIG. 4) control begins at step S500 and continues to step S510. In step S510, for each position of the running window, the number of voxels within a given threshold range is counted. Next, in step S520, the density score of the central voxel of the window is output. This is done by subroutines S522 and S524. Specifically, in step S522, a density score for each operator position is determined. Then, at step S524, if the central voxel falls within a specified threshold range, a density score is calculated, with higher density scores being associated with voxels of high confidence and lower density scores being associated with voxels of lower confidence.

在步骤S530，例如，输出和保存密度分数体作为体积置信度估计。控制然后继续到步骤S540，在此控制序列结束。In step S530, for example, a density score volume is output and saved as a volume confidence estimate. Control then continues to step S540, where the control sequence ends.

图26-30图示应用到地震数据的在此公开的各种示例性技术。然而，应该认识到，在此公开的技术还可以用于其它类型的数据，如医学成像数据、表示物品、身体、(各)对象的2D或3D数据等。26-30 illustrate various exemplary techniques disclosed herein as applied to seismic data. However, it should be appreciated that the techniques disclosed herein may also be used for other types of data, such as medical imaging data, 2D or 3D data representing items, bodies, object(s), etc.

图26图示应用到连贯性的体素密度。面板(a)包含海底峡谷的连贯性图像。面板(b)示出应用二进制体素密度滤波到面板(a)中数据的结果。分配给最小密度阈值测试失败的体素空值。面板(c)示出体素密度受控平滑的结果。体素密度分数用于改变面板(d)中的数据对比。体素密度受控平滑和对比增强保持数据的原始环境，而不是简单移除密度阈值测试失败的体素。Figure 26 illustrates voxel density applied to coherence. Panel (a) contains a coherent image of the submarine canyon. Panel (b) shows the result of applying binary voxel density filtering to the data in panel (a). Null value assigned to voxels that fail the minimum density threshold test. Panel (c) shows the results of voxel density controlled smoothing. Voxel density fractions were used to alter the data contrast in panel (d). Voxel density controlled smoothing and contrast enhancement preserves the original context of the data, rather than simply removing voxels that fail the density threshold test.

图27图示体素抑制的示例。面板(a)包含原始振幅部分。水平平坦盐体的顶部和底部由面板(a)中的箭头指示。面板(b)示出应用体素抑制到面板(a)中数据的结果。已经保持更高振幅事件(包括与盐体相关联的那些)，同时已经移除围绕低振幅的多数。一些分散的反射片段保留(如由面板(b)中的箭头指示的)。Figure 27 illustrates an example of voxel suppression. Panel (a) contains raw amplitude components. The top and bottom of the horizontal flat salt body are indicated by the arrows in panel (a). Panel (b) shows the result of applying voxel suppression to the data in panel (a). Higher amplitude events (including those associated with salt bodies) have been maintained, while the majority surrounding lower amplitudes have been removed. Some scattered reflection fragments remain (as indicated by the arrows in panel (b)).

图28图示反射衰减的结果的示例。具体地，反射衰减技术应用于稀疏地震数据。面板(a)包含体素抑制结果。数据现在是稀疏的。该稀疏改进反射衰减的性能(面板(b))。面板(c)和(d)分别在该示例性反射衰减操作中将仅考虑峰值或波谷的增加限制的情况下处理。面板(c)包含用于突出由面板(a)中的箭头指示的平坦盐体的最佳结果。FIG. 28 illustrates an example of the results of reflection decay. Specifically, reflection attenuation techniques are applied to sparse seismic data. Panel (a) contains voxel-wise suppression results. Data is now sparse. This sparsification improves the performance of reflection decay (panel (b)). Panels (c) and (d) are processed with the added limitation that only peaks or troughs, respectively, will be considered in this exemplary reflection attenuation operation. Panel (c) contains the best results for highlighting the flat salt bodies indicated by the arrows in panel (a).

图29图示体素连通性技术的示例。面板(a)包含体素抑制结果。水平平坦盐体的顶部和底部由面板(a)中的箭头指示。面板(b)示出移除所有由少于200体素组成的连通体素体的结果。面板(c)包含滤出所有具有少于800成分体素的连通体的更好结果。Figure 29 illustrates an example of a voxel connectivity technique. Panel (a) contains voxel-wise suppression results. The top and bottom of the horizontal flat salt body are indicated by the arrows in panel (a). Panel (b) shows the result of removing all connected voxel volumes consisting of less than 200 voxels. Panel (c) contains better results filtering out all connected volumes with less than 800 component voxels.

图30图示示例性属性序列。在该属性序列中，在此描述的整个工作流应用到真实数据。面板(a)包含来自北海数据体的振幅片。盐的顶部位置由面板(a)中的箭头指示。面板(b)示出应用体素抑制到面板(a)中示出的数据的结果。注意到非盐反射的多数已经被滤出(如由箭头所突出的)。应用反射衰减到体素抑制输出的结果在面板(c)中示出。面板(d)包含通过体素密度滤波的最终结果。由面板(c)中箭头突出的分散的反射片段已经被移除。Figure 30 illustrates an exemplary attribute sequence. In this property sequence, the entire workflow described here is applied to real data. Panel (a) contains amplitude slices from the North Sea data volume. The top position of the salt is indicated by the arrow in panel (a). Panel (b) shows the result of applying voxel suppression to the data shown in panel (a). Note that the majority of non-salt reflections have been filtered out (as highlighted by the arrows). The result of applying reflection decay to the voxel-suppressed output is shown in panel (c). Panel (d) contains the final results filtered by voxel density. Scattered reflection fragments highlighted by arrows in panel (c) have been removed.

尽管已经关于特定事件的序列讨论了上述流程图，但是应该认识到，可以出现对于这些序列的改变而不本质上影响本发明的操作。此外，事件的实际序列不需要如在示例性实施例中阐述的那样出现。此外，在此说明的示例性技术不限于具体说明的实施例，而是还可以在其它示例性实施例的情况下被利用，并且每个描述的特征是可个别和分开要求保护的。Although the above flowcharts have been discussed with respect to a particular sequence of events, it should be appreciated that changes to these sequences may occur without materially affecting the operation of the invention. Moreover, the actual sequence of events need not occur as set forth in the exemplary embodiments. Furthermore, the exemplary techniques described herein are not limited to the specifically illustrated embodiments, but may also be utilized in the context of other exemplary embodiments, and each described feature is individually and separately claimable.

本发明的系统、方法和技术可以在专用计算机、编程的微处理器或微控制器和(各)外围集成电路元件、ASIC或其它集成电路、数字信号处理器、硬布线电子或逻辑电路(如离散元件电路)、可编程逻辑器件(如PLD、PLA、FPGA、PAL)或者任何装置等上实现。通常，能够实现反过来能够实现在此说明的方法的状态机的任何设备，可以用于实现根据本发明的各种方法和技术。The systems, methods, and techniques of the present invention can be implemented on special purpose computers, programmed microprocessors or microcontrollers and peripheral integrated circuit component(s), ASIC or other integrated circuits, digital signal processors, hardwired electronic or logic circuits (such as Discrete component circuits), programmable logic devices (such as PLD, PLA, FPGA, PAL) or any device. In general, any device that can implement a state machine that in turn can implement the methods described herein can be used to implement the various methods and techniques in accordance with the present invention.

此外，所公开的方法可以容易地使用对象或面向对象的软件开发环境在处理器可执行软件中实现，所述软件开发环境提供可以在各种计算机或工作站平台上使用的便携式源代码。可替代地，所公开的系统可以部分或全部以使用标准逻辑电路或VLSI设计的硬件实现。是软件还是硬件用于实现根据本发明的系统取决于系统的速度和/或效率要求、特定功能、以及利用的特定软件或硬件系统或微处理器或微计算机系统。在此说明的系统、方法和技术可以容易地由来自在此提供的功能性描述的可应用领域、并且具有计算机和地质领域的一般基础知识的普通技术人员，以使用任何已知或之后开发的系统或结构、设备和/或软件的硬件和/或软件来实现。Furthermore, the disclosed methods can be readily implemented in processor-executable software using an object or object-oriented software development environment that provides portable source code that can be used on a variety of computer or workstation platforms. Alternatively, the disclosed system may be implemented partially or fully in hardware using standard logic circuits or VLSI design. Whether software or hardware is used to implement a system according to the present invention depends on the speed and/or efficiency requirements of the system, the particular functionality, and the particular software or hardware system or microprocessor or microcomputer system utilized. The systems, methods, and techniques described herein can be readily implemented by one of ordinary skill in the field of applicability of the functional description provided herein, and with a general basic knowledge of the computer and geological fields, using any known or later developed system or structure, device and/or software in hardware and/or software.

此外，所公开的方法可以容易地以软件实现，所述软件可以存储在计算机可读存储介质上，在使用控制器和存储器的协作的编程的通用计算机、专用计算机、微处理器等上执行。本发明的系统和方法可以以C或C++、Fortran等实现为嵌入个人计算机的程序，如小应用程序、

或CGI脚本，作为驻留在服务器或计算机工作站上的资源，作为嵌入专用系统或系统组件的例程等。系统还可以通过物理地将系统和/或方法并入软件和/或硬件系统(如专用地震解释设备的硬件和软件系统)来实现。Furthermore, the disclosed methods can be readily implemented in software that can be stored on a computer readable storage medium and executed on a programmed general purpose computer, special purpose computer, microprocessor, etc. using the cooperation of a controller and memory. The system and method of the present invention can be implemented as programs embedded in personal computers with C or C++, Fortran, etc., such as applets,

or CGI scripts, as a resource residing on a server or computer workstation, as a routine embedded in a dedicated system or system component, etc. The system may also be implemented by physically incorporating the system and/or method into a software and/or hardware system, such as that of a dedicated seismic interpretation device.

因此，显而易见的是已经提供了根据本发明的、用于解释数据的系统和方法。尽管已经结合多个实施例描述了本发明，但是明显的是许多替换、修改和变化对于可应用领域的普通技术人员将是或者是显而易见的。因此，旨在包括在本发明的精神和范围内的所有这种替换、修改、等价物和变化。Thus, it should be apparent that there has been provided a system and method for interpreting data in accordance with the present invention. While the invention has been described in conjunction with a number of embodiments, it is evident that many alternatives, modifications and variations would be or will be apparent to those skilled in the applicable arts. Accordingly, it is intended to embrace all such alternatives, modifications, equivalents and variations that fall within the spirit and scope of the invention.